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Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:

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Related Experiment Videos

FreqMamba: Spatial-Frequency Fusion and State Space Sequence Modeling for Deepfake Detection.

Zhiqi Li1, Yajun Chen1, Mingrui Li1

  • 1School of Computer Science, China West Normal University, Nanchong 637009, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

FreqMamba enhances deepfake detection by combining spatial and frequency analysis. This novel framework achieves superior cross-domain generalization for face forgery detection, improving social credibility and privacy protection.

Keywords:
cross-domain generalizationdeepfake detectiondiscrete wavelet transformmambaspatial–frequency fusionstate space model

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Forensics

Background:

  • Deepfake technology poses significant threats to social trust and personal privacy.
  • Current deepfake detection methods struggle with cross-domain generalization due to limitations in spatial and frequency domain analyses.
  • Imperceptible forgery artifacts and ineffective feature integration hinder existing detection algorithms.

Purpose of the Study:

  • To develop an advanced face forgery detection framework with strong cross-domain generalization capabilities.
  • To address the limitations of existing spatial-domain and frequency-aware deepfake detection methods.
  • To propose FreqMamba, an end-to-end framework integrating spatial semantic and frequency-domain features.

Main Methods:

  • Proposed FreqMamba framework utilizing a CNN branch for spatial semantic features.
  • Incorporated a hierarchical discrete wavelet transform (DWT) branch for fine-grained frequency artifact analysis.
  • Employed a bidirectional vision state space model (Vim) for global sequence modeling with linear complexity and a gated late-fusion mechanism.

Main Results:

  • FreqMamba achieved 0.7767 AUC on Celeb-DF v2, outperforming a CNN baseline by 5.05%.
  • On the WildDeepfake dataset, FreqMamba attained 0.6993 AUC, exceeding a lightweight CNN baseline by 7.21%.
  • Ablation studies and Grad-CAM visualizations confirmed the synergistic effects of DWT and Mamba, and the model's focus on critical forgery regions.

Conclusions:

  • FreqMamba offers an effective solution for generalized face forgery detection across different domains.
  • The integration of spatial semantics, frequency artifacts, and global sequence modeling enhances detection performance.
  • The proposed framework significantly improves cross-domain generalization, crucial for real-world deepfake detection applications.